Big Data is Like a Teenage Sex! A Real Life Example of How Big-Data is used in Shipping Industry.



  • The word on every corporate lips to boost business
  • The term every new business blossoms with
  • An idea every start-up aspires to achieve

But wait!

Do any of them really know what “Big-Data” is?

There has been lots of definitions thrown around to explain “Big-Data”.  Surprisingly, the below image is the talk of every social media now.


So what really is a “Big-Data”?

Big data is a collection of data from traditional and digital sources inside and outside your company that represents a source for ongoing discovery and analysis – according to Forbes .

And the “Big-Data” usually revolves around the three Vs.

  • Volume of data,
  • Velocity or the speed with which such data is gathered, &
  • Variety of data.

The present trend is that most Organizations take big decisions with the advent of Big-data! Many claim that they are data scientists and boast that they do wonders with all the data gathered, improving organizational productivity and thus the profit.

What do you think a data scientist or just a data aggregator can do in the world of marine engineering without a basic knowledge of shipping or marine engineering?

In a way, we can say that every Superintendent and Ship Manager is a potential data scientist provided they have a data aggregating tool like VEEMS.

All the data aggregators are just those who are well versed in using the data analytics tool, which obviously would have been designed by them.  Somehow, it got hard grained into people’s mind that all they require is just a powerful processor with a state-of-the-art data analytics tool to process all the data at hand and throw out results.  A word of caution – No tool is just going to process all these data as we wish and throw results or solutions out for you!

When I searched Google, every web page would say that shipping industry is yet to witness any big profitability with ‘Big-data’ and other doldrums, where as coming across one practical way where ‘Big-data’ yielded a fruitful result and measured proven saving is rare.  Most of the data analytics projects, at this point, are more oriented towards fuel savings, energy efficiency and performance management.

Are there any real life example for ‘Big-data’ being employed in the Maritime Industry?

The answer is ‘Yes’ and we at MFAME got one success story to share here with our subscribers.  The team took some special efforts to understand and explain it in simple terms, exclusively for MFAME subscribers.

Bunker fuels are being tested at various laboratories worldwide to check its quality conformance with ISO 8217 standards.  Let me draw a simple analogy here.  The food what we eat and the fuel which the engine consumes are same.  If you eat a bad, terrible food, seats are reserved for you in the closets. If you eat a good food, you are healthy and stay perfect.  Unfortunately, not all food available or served is good, unless you prepare it with care at home.  Think in same terms for the bunker fuels. When a good fuel is supplied, the engine is happy and it works fine.  Supply a bad, adulterated fuel, the poor seafarers suffer cleaning filters and blackouts at mid sea.

Viswa Lab – identified that using bad fuel results in piston ring breakage, which was confirmed by ship operators and managers in more than 85% of the cases.  We approached Viswa Lab and probed them to give more insights on how did they use “Big-data” when the world was not even bothered about integrity of data.

Viswa Lab proudly shared a session with us in explaining the real use of data to find out the root cause of a problem.


Many residual fuel samples are being tested at Viswa Lab.  Though many fuels fall within the ISO 8217 specifications, it caused piston rings to break leading to propulsion failure and other complications.

An attempt was made to pin down the reason for piston ring breakage though the fuel was within the specifications.  In more than 85% to 90% of the cases where the ship operators reported a piston ring breakage, the fuel was high in 5 parameters.  Note that Viswa Lab tests a fuel sample and there are 25+ parameters being reported.

Those 5 parameters which were high in 85% to 90% cases where:

  1. Asphaltenes
  2. CCAI
  3. MCR (Micro Carbon Residue)
  4. Xylene Equivalent
  5. Reserve Stability Number (RSN)

It is to be noted that only CCAI and MCR are part of regular ISO specification tests.  When Asphaltenes, CCAI & MCR are high, then additional tests of Xylene Equivalence and RSN were being done.

Fuels having high MCR(greater than 11.5%), high asphaltene (greater than 10.5%) and high CCAI (greater than 849) were found to cause main engine piston ring breakage.  However, there were a few cases where even when this combination was present, piston rings did not break.  While studying the mechanism of the piston ring breakage, it was clear that the high MCR and the high asphaltene packed the gap between the piston ring and groove with carbonaceous material.  This renders the piston ring immobile leading to eventual fracture of the rings.  The need for finding additional fuel quality parameters which, could effectively pin down the problem fuels was felt.  Viswa Lab was able to identify Xylene Equivalent number and Reserve Stability Number as two other parameters which in combination with the three listed above, clearly flagged fuels likely to cause piston ring breakage with 85 to 90% certainty.  There is enough statistical data to confirm the findings says Viswa Lab.


Please refer to graph # 1. The red line indicates cases where there is a 85 to 90% certainty of piston ring breakage.  The green line has values similar to those of the red line but piston ring breakage may or may not take place.  The line in blue is the values for which there is absolutely no chance of any piston ring breakage.  It will be noticed that the red and green graphs have values near to each other and there is a chance of a same set of values appearing in both graphs.  Viswa Lab, therefore generated an algorithm so that the values will be distinctly different with no interference.


Graph # 2 shows the red line distinct from the green one.  Using this algorithm it is possible to obtain a number from the values for five parameters, namely MCR, CCAI, Asphaltene, Xylene Equivalent and Reserve Stability Number and if the number exceeds a certain value (greater than 125), there would be an 85 to 90% chance of piston ring breakage. “Ideally, we would like to have at least 40 data points for each graph that means 120 data points in total.  We are collecting these data points and we can confidently announce that this number and the algorithm will work with 85 to 90% certainty”, Viswa Lab reported few years ago.

Viswa Lab is proud that their benchmark, PFIN (Problem Fuel Identification Number) is the only benchmark to find out whether a fuel under study can cause a piston ring breakage.  This is a serious problem endangering the safety of the vessel when the renewal of piston rings has to be carried out while the ship drifts at sea.

Further, not just identifying the suspect fuel to cause a piston ring breakage, but also offer actions for mitigating the problem is core-strength thereby helping many ship Managers in solving fuel related problems.

We are sure that all the Superintendents, Technical and Fleet Managers, who read this write-up will definitely agree and recollect these five tests and PFIN algorithm.

Is this an example of brilliant ‘Big-data’ analytics to solve the problem of piston ring breakage?

Share your views and voice your opinion!

We thank Viswa Lab for  providing relevant images and respective technical insights for the piston ring breakage problem.


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